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name: filtering-segmentation description: "Apply SimpleITK filters for smoothing, thresholding, morphology, segmentation, N4 bias correction, and label-statistics validation." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

SimpleITK Filtering and Segmentation

Use this sub-skill when a task asks to apply SimpleITK filters procedurally or with *ImageFilter objects, smooth or threshold an image, run connected/confidence region growing, use fast marching, correct bias with N4, inspect filter parameters, or validate segmentation output with label statistics.

Route by Task

• For choosing between procedural sitk.FilterName(image, ...) calls and reusable sitk.FilterNameImageFilter() objects, use filter-patterns.
• For source image generators, dimensional parameter vectors, event/progress callbacks, and reusable filter-object templates, use filter-patterns.
• For Otsu/manual thresholding, connected/confidence connected region growing, fast marching, N4 correction, and label-statistics validation, use segmentation-recipes.
• For representative Python signatures, defaults, and compact examples for smoothing, thresholding, segmentation, N4, and statistics filters, use api-reference.
• For pixel type and dimension errors, seed or mask mistakes, expensive filters, validation surprises, and accidental viewer calls, use troubleshooting.
• To check a local install with tiny synthetic images and no file IO or viewer, run filter_segmentation_smoke.py.

Boundary Notes

• Use the sibling io-and-data sub-skill for ReadImage, WriteImage, DICOM discovery, ImageIO choices, and transform file IO.
• Use the sibling image-core sub-skill for pixel ID selection, image dimensions, spacing/origin/direction, metadata, and array/image conversion fundamentals.
• Use the sibling registration-transforms sub-skill for registration frameworks, transform initialization, resampling semantics, and transform composition.
• Do not rely on optional elastix/transformix wrappers for filtering workflows; they are build-dependent and were not exposed by the inspected wheel.

Common Entry Points

• sitk.GaussianSource(...) creates deterministic synthetic images for examples and smoke tests.
• sitk.SmoothingRecursiveGaussian(image, sigma, normalizeAcrossScale=False) and sitk.DiscreteGaussian(image, variance, ...) handle Gaussian smoothing with different performance/spacing behavior.
• sitk.BinaryThreshold(...), sitk.OtsuThreshold(...), sitk.ConnectedThresholdImageFilter(), and sitk.ConfidenceConnectedImageFilter() cover common binary and seeded segmentation workflows.
• sitk.FastMarchingImageFilter() creates arrival-time maps from trial points; threshold the time map to obtain a segmentation.
• sitk.N4BiasFieldCorrectionImageFilter() corrects slowly varying intensity bias when given a real-valued image and usually a mask.
• sitk.LabelShapeStatisticsImageFilter() validates label images by object count, bounding boxes, centroids, physical size, perimeter, and shape metrics.